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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1951–2000 of 2226 papers

TitleStatusHype
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data AugmentationCode0
Revisiting Bayesian Autoencoders with MCMCCode0
Reweighted Expectation MaximizationCode0
Bistable perception, precision and neuromodulationCode0
Riemannian Laplace Approximation with the Fisher MetricCode0
Bijectors.jl: Flexible transformations for probability distributionsCode0
Riemannian Stein Variational Gradient Descent for Bayesian InferenceCode0
AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithmsCode0
BAMBI: blind accelerated multimodal Bayesian inferenceCode0
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic LikelihoodCode0
Multilevel neural simulation-based inferenceCode0
Fidelity of Hyperbolic Space for Bayesian Phylogenetic InferenceCode0
Fine-Tuning Generative Models as an Inference Method for Robotic TasksCode0
Bayesian inference via sparse Hamiltonian flowsCode0
Robust and Conjugate Gaussian Process RegressionCode0
Robust and Scalable Variational BayesCode0
MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physicsCode0
Robust Anytime Learning of Markov Decision ProcessesCode0
Fitting A Mixture Distribution to Data: TutorialCode0
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexityCode0
Flexible Amortized Variational Inference in qBOLD MRICode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Automated Scalable Bayesian Inference via Hilbert CoresetsCode0
Stochastic Gradient Descent as Approximate Bayesian InferenceCode0
Bidirectional Convolutional Poisson Gamma Dynamical SystemsCode0
Accelerating Convergence of Stein Variational Gradient Descent via Deep UnfoldingCode0
Flexible statistical inference for mechanistic models of neural dynamicsCode0
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior BootstrapCode0
Mutually Regressive Point ProcessesCode0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPsCode0
A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare DataCode0
Stochastic gradient Markov chain Monte CarloCode0
Stochastic Gradient MCMC for Nonlinear State Space ModelsCode0
Near-Optimal Approximations for Bayesian Inference in Function SpaceCode0
Better Peer Grading through Bayesian InferenceCode0
Negative Binomial Process Count and Mixture ModelingCode0
Stochastic Gradient MCMC for State Space ModelsCode0
Network Plasticity as Bayesian InferenceCode0
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence ClusteringCode0
Bayesian Inference Semantics: A Modelling System and A Test SuiteCode0
A generative flow for conditional sampling via optimal transportCode0
Robust Generalised Bayesian Inference for Intractable LikelihoodsCode0
Being a Bit Frequentist Improves Bayesian Neural NetworksCode0
From Understanding the World to Intervening in It: A Unified Multi-Scale Framework for Embodied CognitionCode0
β-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of OutliersCode0
BClean: A Bayesian Data Cleaning SystemCode0
Full-waveform earthquake source inversion using simulation-based inferenceCode0
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in BadlandsCode0
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal NeuronsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified